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RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://arxiv.org/abs/1907.11692

Introduction

RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details.

Pre-trained models

Model Description # params Download
roberta.base RoBERTa using the BERT-base architecture 125M roberta.base.tar.gz
roberta.large RoBERTa using the BERT-large architecture 355M roberta.large.tar.gz
roberta.large.mnli roberta.large finetuned on MNLI 355M roberta.large.mnli.tar.gz

Results

Results on GLUE tasks (dev set, single model, single-task finetuning)
Model MNLI QNLI QQP RTE SST-2 MRPC CoLA STS-B
roberta.base 87.6 92.8 91.9 78.7 94.8 90.2 63.6 91.2
roberta.large 90.2 94.7 92.2 86.6 96.4 90.9 68.0 92.4
roberta.large.mnli 90.2 - - - - - - -
Results on SQuAD (dev set)
Model SQuAD 1.1 EM/F1 SQuAD 2.0 EM/F1
roberta.large 88.9/94.6 86.5/89.4
Results on Reading Comprehension (RACE, test set)
Model Accuracy Middle High
roberta.large 83.2 86.5 81.3

Example usage

Load RoBERTa from torch.hub (PyTorch >= 1.1):
import torch
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
roberta.eval()  # disable dropout (or leave in train mode to finetune)
Load RoBERTa (for PyTorch 1.0):
# Download roberta.large model
wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
tar -xzvf roberta.large.tar.gz

# Load the model in fairseq
from fairseq.models.roberta import RobertaModel
roberta = RobertaModel.from_pretrained('/path/to/roberta.large')
roberta.eval()  # disable dropout (or leave in train mode to finetune)
Apply Byte-Pair Encoding (BPE) to input text:
tokens = roberta.encode('Hello world!')
assert tokens.tolist() == [0, 31414, 232, 328, 2]
roberta.decode(tokens)  # 'Hello world!'
Extract features from RoBERTa:
# Extract the last layer's features
last_layer_features = roberta.extract_features(tokens)
assert last_layer_features.size() == torch.Size([1, 5, 1024])

# Extract all layer's features (layer 0 is the embedding layer)
all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
assert len(all_layers) == 25
assert torch.all(all_layers[-1] == last_layer_features)

By default RoBERTa outputs one feature vector per BPE token. You can instead realign the features to match spaCy's word-level tokenization with the extract_features_aligned_to_words method. This will compute a weighted average of the BPE-level features for each word and expose them in spaCy's Token.vector attribute:

doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."')
assert len(doc) == 10
for tok in doc:
    print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
# <s>       tensor([-0.1316, -0.0386, -0.0832, -0.0477,  0.1943], grad_fn=<SliceBackward>) (...)
# I         tensor([ 0.0559,  0.1541, -0.4832,  0.0880,  0.0120], grad_fn=<SliceBackward>) (...)
# said      tensor([-0.1565, -0.0069, -0.8915,  0.0501, -0.0647], grad_fn=<SliceBackward>) (...)
# ,         tensor([-0.1318, -0.0387, -0.0834, -0.0477,  0.1944], grad_fn=<SliceBackward>) (...)
# "         tensor([-0.0486,  0.1818, -0.3946, -0.0553,  0.0981], grad_fn=<SliceBackward>) (...)
# hello     tensor([ 0.0079,  0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...)
# RoBERTa   tensor([-0.2339, -0.1184, -0.7343, -0.0492,  0.5829], grad_fn=<SliceBackward>) (...)
# .         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
# "         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
# </s>      tensor([-0.0930, -0.0392, -0.0821,  0.0158,  0.0649], grad_fn=<SliceBackward>) (...)
Use RoBERTa for sentence-pair classification tasks:
# Download RoBERTa already finetuned for MNLI
roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
roberta.eval()  # disable dropout for evaluation

# Encode a pair of sentences and make a prediction
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')
roberta.predict('mnli', tokens).argmax()  # 0: contradiction

# Encode another pair of sentences
tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')
roberta.predict('mnli', tokens).argmax()  # 2: entailment
Register a new (randomly initialized) classification head:
roberta.register_classification_head('new_task', num_classes=3)
logprobs = roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
Batched prediction:
from fairseq.data.data_utils import collate_tokens
sentences = ['Hello world.', 'Another unrelated sentence.']
batch = collate_tokens([roberta.encode(sent) for sent in sentences], pad_idx=1)
logprobs = roberta.predict('new_task', batch)
assert logprobs.size() == torch.Size([2, 3])
Using the GPU:
roberta.cuda()
roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
Filling mask:

Some examples from the Natural Questions dataset.

>>> roberta.fill_mask("The first Star wars movie came out in <mask>", topk=3)
[('The first Star wars movie came out in 1977', 0.9504712224006653), ('The first Star wars movie came out in 1978', 0.009986752644181252), ('The first Star wars movie came out in 1979', 0.00957468245178461)]

>>> roberta.fill_mask("Vikram samvat calender is official in <mask>", topk=3)
[('Vikram samvat calender is official in India', 0.21878768503665924), ('Vikram samvat calender is official in Delhi', 0.08547217398881912), ('Vikram samvat calender is official in Gujarat', 0.07556255906820297)]

>>> roberta.fill_mask("<mask> is the common currency of the European Union", topk=3)
[('Euro is the common currency of the European Union', 0.945650577545166), ('euro is the common currency of the European Union', 0.025747718289494514), ('€ is the common currency of the European Union', 0.011183015070855618)]
Evaluating the roberta.large.mnli model

Example python code snippet to evaluate accuracy on the MNLI dev_matched set.

label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
ncorrect, nsamples = 0, 0
roberta.cuda()
roberta.eval()
with open('glue_data/MNLI/dev_matched.tsv') as fin:
    fin.readline()
    for index, line in enumerate(fin):
        tokens = line.strip().split('\t')
        sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
        tokens = roberta.encode(sent1, sent2)
        prediction = roberta.predict('mnli', tokens).argmax().item()
        prediction_label = label_map[prediction]
        ncorrect += int(prediction_label == target)
        nsamples += 1
print('| Accuracy: ', float(ncorrect)/float(nsamples))
# Expected output: 0.9060

Finetuning

Pretraining using your own data

You can use the masked_lm task to pretrain RoBERTa from scratch, or to continue pretraining RoBERTa starting from one of the released checkpoints.

Data should be preprocessed following the language modeling example.

A more detailed tutorial is coming soon.

Citation

@article{liu2019roberta,
    title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
    author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
              Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
              Luke Zettlemoyer and Veselin Stoyanov},
    journal={arXiv preprint arXiv:1907.11692},
    year = {2019},
}